Even though the vast majority of the sentiment analysis proposals focus on single model sentiment analysis, mainly using textual information [1]}, [2]}, there are interesting proposals that try to fuse more than one source of information to perform multimodal sentiment analysis. The method proposed in [3]}, combines features from audio and video, and fuses them with text features to estimate the sentiment of youtube movie reviews. In [4]}, the authors propose Tensor Fusion Network, which is a network capable of fusing features extracted from different sources of data, into a single tensor, allowing sentiment analysis both in separate and conjoined. Taking a different approach [5]}, performs multimodal sentiment analysis by conducting hierarchical fusion of the different features by first fusing them into pairs, and then combining them into one. In [6]}, the authors proposed Visual Aspect Attention Network, in which the goal is to use images as attention mechanisms to aid in detecting important sentences in documents. To perform such operation, images are analyzed using a CNN, and the output is used as weights in a word encoder. In [7]}, the authors propose a multimodal method that performs individual analysis of both the image and the text, and then performs a weighted average over the individual predictions to perform a final classification. However, while producing the individual predictions, the authors also introduce Image Content Analysis, a second method to classify the images, which is based on detecting the most predominant object on the image and classifying the image based on the probability of that object appearing in a given class in the training set.
